In many instances, consumers do not have information relevant to a specifically desired product or do not understand such information. Exacerbating this problem is the fact that complex, negotiated transactions can be difficult for consumers to understand due to a variety of factors, including interdependence between local demand and availability of products or product features, the point-in-time in the product lifecycle at which a transaction occurs, and the interrelationships of various transactions to one another.
Sellers may experience similar difficulties but from an opposite perspective. It is often time difficult to determine or predict the behavior of buyers. This difficulty in no small part stems from the fact that behavioral patterns of buyers vary widely with geography. These circumstances can be seen in a variety of contexts. In particular, the automotive transaction process may entail complexity of this type, as the distribution of dealers and consumers can vary widely based on geography.
However, these circumstances have not tempered the desired for effective analysis of the vehicle marketplace. Historically, the vehicle market was analyzed defining distance brackets (e.g. 15, 30 and 60 miles radii) and all performance indicators for data analysis in the vehicle marketplace were calculated for those distance brackets (e.g. close rate in 15 miles; conversion rates in 60 miles around a zip code) for the whole nation, with no regard to the relevance of such distances to the local market. This methodology rendered rather poor predictions.
These poor predictions are not surprising at least because, as discussed, behavioral patterns vary across the nation due to population and car dealer densities, as well as connectivity (e.g., number and types of roads or other transport mechanisms). As a consequence, a journey of 30 miles (e.g., to a vehicle dealer) or more in rural areas is rather common, whereas such a distance is far beyond the typical journey of an urban customer. Even if urban customers are considered, however, the typical distance driven varies by neighborhood and car brand (make). For example, the distance traveled for a consumer to find an Alfa Romeo dealership may typically be much farther than the distance traveled to find a Ford dealership, even for urban consumers. Thus, behavioral patterns vary across the nation due to population and car dealer densities as well as connectivity.
As market key indicators (e.g., demand, conversion and close rates, market share, etc.) are currently determined based on distance of dealers or consumers, all predictions are subject to substantial noise coming from the variability described above, hence the prediction accuracy of such indicators or other values are rather low. As one example, when it is desired to predict close rate based on distance, samples from rural places will have highly different close rates than samples from urban environments for the exact same distance. This is detrimental to prediction accuracy since it introduces noise.
On the other hand, if it is decided to segment by region type, prohibitively small samples for some makes may result. The current methods for the determination of market indicators in the vehicle sales context thus adversely affects the abilities of participants in the industry to provide accurate analysis of the marketplace. This situation is particularly germane to those participants that may maintain networks of dealerships or provide dealer or consumer facing products that rely on the accuracy of those marketplace analytics, such as TrueCar, Inc.
There are therefore a number of unmet desires when it comes to obtaining, analyzing and presenting vehicle pricing data. In particular, it is desired to provide metrics that account for density of population and density of dealerships for various makes in the context of the vehicle marketplace. Specifically, what is desired are computerized systems and methods for determining such metrics that can obtain, manage and process large amounts of data available across a wide variety of distributed computer systems and efficiently process obtained data to establish high-fidelity metrics that are accurately reflective of real-world conditions and that may be used to distribute market indicators or other data across a network in real-time.